If you're not running against the SQLite test suite, then you haven't written a viable SQLite replacement.
I'm not sure where they get their 90k CLOC count though, that seems like it might be an LLM induced hallucination given the rest of the project. The public domain TCL test suite is ~27k CLOC, and the proprietary suite is 1055k CLOC.
The value of SQLite is how robust it is and that’s because of the rigorous test suite.
I'm not sure where they get their 90k CLOC count though, that seems like it might be an LLM induced hallucination given the rest of the project. The public domain TCL test suite is ~27k CLOC, and the proprietary suite is 1055k CLOC.
> and the proprietary suite is 1055k CLOC.
Why is the code size of the proprietary test suite even public though?
Any serious SQLite re-implementation should buy it and test against it.
It's much more likely the issue is one of cost, not of seriousity.
Single writer will outperform MVCC as long as you do dynamic batching (doesn't prevent logical transactions) and all you have to do is manage that writer at the application level.
Concurrent writers just thrash your CPU cache. The difference between L1 and L3 can be 100x. So your single writer on a single core can outperform 10-100s of cores. Especially when you start considering contention.
Here's sqlite doing 100k TPS and I'm not even messing with core affinity and it's going over FFi in a dynamic language.
https://andersmurphy.com/2025/12/02/100000-tps-over-a-billio...
https://github.com/Dicklesworthstone/frankensqlite#current-i...
Although I will admit that even after reading it, I'm not exactly sure what the current implementation status is.
Utterly unmaintainable by any human, likely never to be completed or used, but now deposited into the atmosphere for future trained AI models and humans alike to stumble across and ingest, degrading the environment for everyone around it.
But nobody shows off static HTML sites on HN.
RS over GF256 is more than adequate. Or just plain LDPC.
MIT plus a condition that designates OpenAI and Anthropic as restricted parties that are not permitted to use or else?
Also it's pretty hilarious to vibe-code a library that clones another library that someone has spent decades of work on, and then try to prohibit people from using that LLM output as training data for an LLM.
Impressive piece of work from the AIs here.
Are there any other FrankenProjects out there that have had any success?
Were we so impressed by the concept of the original Frankenstein?
Is this a Freudian slip, that we are expecting these AI projects to turn on their creators?
A better question is if the implementation was touched by anything other than generative AI.